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A local distributed peer-to-peer algorithm using multi-party optimization based privacy preservation for data mining primitive computation

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3 Author(s)
Das, K. ; Univ. of Maryland Baltimore County, Baltimore, MD, USA ; Kargupta, H. ; Bhaduri, K.

This paper proposes a scalable, local privacy-preserving algorithm for distributed peer-to-peer (P2P) data aggregation useful for many advanced data mining/analysis tasks such as average/sum computation, decision tree induction, feature selection, and more. Unlike most multi-party privacy-preserving data mining algorithms, this approach works in an asynchronous manner through local interactions and therefore, is highly scalable. It particularly deals with the distributed computation of the sum of a set of numbers stored at different peers in a P2P network in the context of a P2P Web mining application. The proposed optimization-based privacy-preserving technique for computing the sum allows different peers to specify different privacy requirements without having to adhere to a global set of parameters for the chosen privacy model. Since distributed sum computation is a frequently used primitive, the proposed approach is likely to have significant impact on many data mining tasks such as multi-party privacy-preserving clustering, frequent itemset mining, and statistical aggregate computation.

Published in:

Peer-to-Peer Computing, 2009. P2P '09. IEEE Ninth International Conference on

Date of Conference:

9-11 Sept. 2009

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